Selective rationalization:  [1]} proposes the first generator-predictor framework for rationalization. Following this work, new game-theoretic frameworks were proposed to encourage different desired properties of the selected rationales, such as optimized Shapley structure scores [2]}, comprehensiveness [3]}, multi-aspect supports [4]}, [5]} and invariance [6]}. Another fundamental direction is to overcome the training difficulties. [7]} replaces policy gradient with Gumbel softmax. [3]} proposes to first pre-train the predictor, and then perform end-to-end training. [5]} adopts different updating dynamics between the generator and the predictor. [10]} replaces the Bernoulli sampling distributions with rectified continuous random variables to facilitate constrained optimization. [11]} proposes to enhance the training objective with an adversarial information calibration according to a black-box predictor. However, these methods cannot address the problem of interlocking.
